Method, system, and computer program for user-driven dynamic generation of semantic networks and media synthesis

ABSTRACT

This invention relates generally to classification systems. More particularly this invention relates to a system, method, and computer program to dynamically generate a domain of information synthesized by a classification system or semantic network. The invention discloses a method, system, and computer program providing a means by which an information store comprised of knowledge representations, such as a web site comprised of a plurality of web pages or a database comprised of a plurality of data instances, may be optimally organized and accessed based on relational links between ideas defined by one or more thoughts identified by an agent and one or more ideas embodied by the data instances. Such means is hereinafter referred to as a “thought network”.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a U.S. National Stage entry of International Application Serial No. PCT/CA2009/000567, entitled “Method, System, And Computer Program For User-Driven Dynamic Generation Of Semantic Networks And Media Synthesis” and filed on May 1, 2009, which claims priority to U.S. Provisional Application No. 61/049,581, filed on May 1, 2008.

FIELD OF THE INVENTION

This invention relates generally to classification systems. More particularly this invention relates to a system, method, and computer program to dynamically generate a domain of information synthesized by a classification system or semantic network and to facilitate media synthesis.

BACKGROUND OF THE INVENTION

Semantic networks are often used as forms of knowledge representation. A semantic network is a directed graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between the concepts.

In contrast to a network object, a networking activity comprises the active creation and reorganization of networks. Social networking is one example of a networking activity, wherein a network of acquaintances is built and revised. Members can use such a network to facilitate active interactions with other members. These networking interactions can result in changes to the network.

Whereas semantic networks are directed graphs, semantic networking is the process of developing those graphs.

It has generally been a challenge to those skilled in the art to create representations of abstract knowledge for mass market, consumer-focused activities. One reason for this is that the subject matter of a semantic network in consumer markets is often highly subjective and personal. Unlike organizational settings, it cannot be prescribed or given a universal representation for individual consumers.

In addition, knowledge entities are multifaceted; that is, they can have many different organizational bases (or dimensions). As the number of dimensions increases beyond three or four, representations of multidimensional objects, matrices, or networks quickly become too unwieldy for the cognitive abilities of human users. This problem is compounded when combining the representations of one person with those of others, because each person will have his or her own unique perspectives and vocabularies.

Another difficulty is that of scalability: thoughts and knowledge are unbounded, and representing them poses problems of storage and management.

Furthermore, semantic networking requires a process to guide it. Cognitive agents such as people may direct the production of knowledge representations, but the process must be mediated to be effective. Current approaches of doing so include: ontology-building and taxonomy-building tools; semantic web and collective knowledge initiatives; folksonomies (such as social tagging); semantic extraction (from legacy representations of knowledge); data mining; and others. These approaches require users to modify or accommodate their thinking in support of the technology. To be more effective, the system design should be directed by how people think.

Clearly, semantic networking processes are quite different than, for example, the general notion of semantic networks, which as previously mentioned are merely representations of a state of knowledge. Semantic networks have a broad utility as a form of knowledge representation. As machine-readable data, they can support a number of advanced technologies, such as artificial intelligence, software automation and “agents”, expert systems, and knowledge management. Additionally, they can be transformed into various forms of media (other knowledge representations). In other words, the synthesis or creation of semantic networks can support the synthesis of a broad swath of media to extract additional value from the semantic network.

Some approaches to synthesize media are presently known. For example, NLP/grammar-based/linguistic document structure analysis is utilized as a lattice for collating content components and deducting component linking and alignment to form synthesized media. Additionally, multi-document summarization method is known, whereby common and diverse elements are captured across a number of documents, and merges or organizes these under a common these. Another approach involves utilizing a single super-document (e.g. a content model) and applying document transformations, for example, such as XSLT or XSD, to synthesize smaller subsets of documents. Still another known approach is synthesis in response to a complex specification from a user whereby user requirements are modelled, either implicitly, for example, such as demographic profiles, or explicitly, for example, such as by specific topics or perspectives, to provide a basis for synthesis operations. Other known approaches include social/collaborative/Web 2.0.

The above-listed methods do not provide options for consumers of media to direct the synthesis process. Consumers requiring personally tailored media must either create the document from scratch or use synthesizing approaches that are based upon existing documents or sources.

SUMMARY OF THE INVENTION

In one aspect of the present invention a computer implemented method for generating a semantic network is provided, characterized in that it comprises the steps of: (a) providing an information domain; (b) representing the information domain as a data set, the data set being defined by data entities and one or more relationships between the data entities; (c) providing, by means of an agent, data representing one or more thoughts; and (d) synthesizing, or facilitating the synthesizing, by one or more computer processors, a semantic network based on one or more interactions between the data entities and the one or more thoughts.

In another aspect of the present invention a computer system for generating a semantic network is provided, characterized in that it comprises: (a) one or more computers configured to provide, or provide access to, an information domain, wherein a data set is operable to represent the information domain, the data set being defined by data entities and one or more relationships between the data entities, and wherein an agent is operable to provide data representing one or more thoughts; and (b) a thought processor operable to synthesize, or facilitate the synthesis of, by one or more computer processors, a semantic network based on one or more interactions between the data entities and the one or more thoughts.

In yet another aspect of the present invention a computer program product for enabling the generation of a semantic network accessible through a web interface that enables an agent to initiate one or more computers to generate the semantic network is provided, the computer program product characterized in that the computer program product comprises computer instructions and data for configuring one or more computer processors to: (a) obtain, or obtain access to, an information domain, the information domain being represented by a data set representing the information domain, the data set being defined by data entities and one or more relationships between the data entities, wherein an agent is operable to provide data representing one or more thoughts; and (b) synthesize, or facilitate the application of, by a thought processor, a semantic network based on one or more interactions between the data entities and the one or more thoughts.

In a still other aspect of the present invention a computer implemented method for synthesizing media utilizing a semantic network is provided, characterized in that it comprises the steps of: (a) generating, or facilitating the generation of, by one or more computer processors, a thought network based on one or more interactions between one or more data entities and one or more thoughts; and (b) transforming the thought network so as to generate and provide one or more forms of synthesized media to a consumer.

In a further aspect of the present invention a method for generating a semantic network is provided, characterized in that it comprises the steps of: (a) providing an information domain; (b) representing the information domain as a data set, the data set being defined by data entities and one or more relationships between the data entities; (c) an agent providing data representing one or more thoughts; and (d) synthesizing a semantic network based on one or more interactions between the data entities and the one or more thoughts.

In a yet further aspect of the present invention a system for generating a semantic network is provided, characterized in that it comprises: (a) an information domain; (b) a data set operable to represent the information domain, the data set being defined by data entities and one or more relationships between the data entities; (c) an agent operable to provide data representing one or more thoughts; and (d) a semantic network, the semantic network synthesized based on one or more interactions between the data entities and the one or more thoughts.

In a still further aspect of the present invention a computer program for enabling generation of a semantic network is provided, the computer program characterized in that it comprises computer instructions, which when loaded on a server computer connected to the Internet are operable to provide a web application defining an interface that enables an agent to initiate the server computer to generate the semantic network by: (a) providing an information domain; (b) representing the information domain as a data set, the data set being defined by data entities and one or more relationships between the data entities; (c) the agent providing data representing one or more thoughts; and (d) synthesizing a semantic network based on one or more interactions between the data entities and the one or more thoughts.

In an additional aspect of the present invention a method for generating synthesized media utilizing a semantic network is provided, the method comprising: (a) generating the semantic network by the steps of: (i) providing an information domain; (ii) representing the information domain as a data set, the data set being defined by data entities and one or more relationships between the data entities; (iii) an agent providing data representing one or more thoughts; and (iv) synthesizing a semantic network based on one or more interactions between the data entities and the one or more thoughts; (b) generating the synthesized media by a transformation of the semantic network; and (c) providing the synthesized media to the agent.

In this respect, before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments and of being practiced and carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood and objects of the invention will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:

FIG. 1 illustrates one implementation of the components required to generate and use a thought network.

FIG. 2 illustrates knowledge representation entities and relationships in one implementation of the invention.

FIG. 3 illustrates one example for the process of knowledge representation, which may in this example be referred to as facet analysis.

FIG. 4 illustrates one implementation of the thought network synthesis aspect of the thought processor.

FIG. 5 illustrates one aspect of a user interface provided by the present invention.

FIG. 6 is a flow chart of the process to transform a thought network to produce synthesized media.

FIG. 7 illustrates a user interface whereby synthesized media may be produced.

FIG. 8 illustrates a synthesized media output.

In the drawings, embodiments of the invention are illustrated by way of example. It is to be expressly understood that the description and drawings are only for the purpose of illustration and as an aid to understanding, and are not intended as a definition of the limits of the invention.

DETAILED DESCRIPTION

The present invention, in one aspect thereof, provides a system, method and computer program for the automatic creation of semantic networks and thought networks. The present invention, in another aspect thereof, also provides a system and method for providing the broad requirements for semantic networking, including: (1) a way to digitally represent abstract thoughts; (2) the means to store and recall thoughts (as digital memories); (3) the means to contextualize thoughts within a timely and relevant perspective, linking them to other thoughts within the same perspective (a semantic network); (4) the means to direct those representations of thoughts to specific tasks; and (5) the means to economically store what is learned, to be used for future iterations of semantic networking.

The present invention, in yet another aspect thereof, facilitates a method, system and computer program whereby a media consumer may direct media synthesis to produce one or more tailored documents. In this manner the consumer may personally direct the synthesis process, so that it involves the direct contribution and insights of the individual consumer. The result produced may be personally tailored media. The present invention offers a hybrid solution whereby individual consumers may be the sole author and voice directing the creation of media and its content, and this process may be facilitated by machine-media synthesis which can gather a wide range of documents and document sources and further accelerate the process beyond that which is possible by other means. The result of the hybrid solution may be synthesized media or documents that are unique to each consumer and are not constrained by the boundaries and limits imposed by documents, media sources, and known media synthesis approaches.

Overview of Semantic and Thought Networks

The present invention, in one aspect thereof, discloses a method, system, and computer program providing a means by which an information store comprised of knowledge representations, for example a web site comprised of a plurality of web pages or a database comprised of a plurality of data instances, may be optimally organized and accessed based on relational links between ideas defined by one or more thoughts identified by an agent and one or more ideas embodied by the data instances. Such means is hereinafter referred to as a “thought network”.

Like semantic networking, thought processing is defined by the dynamism of interactions, by the active creation and reshuffling of semantic networks. A “thought processor” may provide this role of processing thoughts to create dynamic, evolving thought networks, or a network comprised of a synthesized domain of information. As new representations of thoughts enter the system, the thought processor may use the new information to infer new thoughts and new connections between thoughts. Unlike existing reasoning and inference engines, a thought processor in accordance with the present invention may draw inferences in the production of semantic networks (creating both nodes and edges), to complement reasoning across existing semantic networks. To accomplish this, a thought processor may integrate the knowledge representations (thought data) with the inputs of cognitive agents (including, for example, people).

Once created, semantic (thought) networks can be put to many uses. Thought networking may address at least three broad classes of activities, including: (1) providing a means to acquire, store, and recall thoughts; (2) providing a means to generate highly personalized, contextualized, and utilitarian thought networks as they are needed to do work; and (3) providing software agents to get those jobs done as they consume the thought networks that have been created.

These thought networks may be useful for several reasons. For example, they can be used to navigate a website that is based on information derived from thought networking thereby enabling Internet searching, aiding in shopping, enabling documents to be outlined, supporting the creation of social networks to be created, etc. The role of software agents may include, but is not limited to: providing data for query-builders that search and retrieve information from the Internet; acting as a shopping agent to locate products that fit specific criteria; outlining documents by representing a semantic network as a document and populating it with content retrieved from different domains; and creating social networks by looking for intersections of thoughts in the semantic networks generated by different people.

FIG. 1 illustrates one implementation of the components required to generate and use a thought network.

In one implementation of the present invention, generation of a thought network occurs by means of the following components: a means of knowledge representation 101, a thought processing facility 103, the use of cognitive agents 105, a thought storage means 107, and an adaptive classification scheme generator 109. Each of these components is described below. Novel representative implementations are described for each component, but a person skilled in the art would recognize that other known implementations of the components may provide for operation of the overall system of the present invention. It should be noted that each of the components may, but need not necessarily, operate simultaneously, to dynamically and optimally refine a thought network.

In another aspect of the present invention, the generation and use of a thought network is enabled in particular by a thought processing facility 103, which is best understood as an implementation of a build engine, for example the build engine described in PCT application PCT/CA2007/001546. The thought processing facility 103 may be linked to a means of concept synthesis 113 also described in PCT application PCT/CA2007/001546. An interface 115 may also be provided for enabling cognitive agents 105 to interact with the thought processing facility 103 in accordance with the processes more fully described below.

In order to distil an information store (or source domain 111) to its knowledge representations, it may first be necessary to abstract the information to a conceptual, semantic representation. A process of information abstraction may be referred to as “knowledge representation”.

The means of knowledge representation 101 may comprise a physical representation of abstract thoughts. In the context of the present invention, a specific and elemental unit of knowledge may be represented and optionally encoded (in one aspect, the elemental unit may be the absolute smallest unit of knowledge available). The artifacts produced by the knowledge representation means may use various media that represent thoughts, for example web pages, RSS feeds, RDF graphs, etc. These media may provide for thought interactions that are processed by other components of the system of the present invention. The media may then be monetized through a wide variety of revenue models: advertising, subscriptions, transactional micro-payments, etc. as taught by the present invention.

Once the units of knowledge have been represented, a thought processing facility 103 may then provide a means of concept synthesis 113. Similar to certain embodiments of social networking, thought processing may be defined by the dynamism of interactions resulting in the active creation and reshuffling of networks (again, thought processing may be quite different than, for example, the notion of “semantic networks”, which is generally a largely static representation of knowledge). A thought processing facility 103 may process thoughts to create dynamic, evolving networks. As new representations of thoughts enter the system, the thought processing facility 103 may utilize the new information to infer new thoughts and new connections between thoughts. Unlike existing reasoning and inference engines, a thought processing facility 103 may be required to draw inferences in the production of semantic networks (creating both nodes and edges, for example), to complement reasoning across existing semantic networks. To accomplish this, a thought processing facility 103 may be required to integrate the knowledge representations (thought data) with the inputs of cognitive agents 105 (also referred to in this disclosure as “agents”), which may be people, machine-based expert systems, etc. The thought processing facility 103 may augment and speed the “thinking” of the cognitive agents 105.

The cognitive agents 105, which may be people, machine-based expert systems, etc., may provide the inputs of pattern recognition and selection that may refine and optimize thought networks over time. In addition to the media produced by the thought networks, cognitive agents 105 may also be the beneficiaries of value creation, through benefits such as enhanced recall or memory, creativity, and associative thinking. Like the media created, these benefits may be monetized through licensing, subscriptions, etc.

A thought storage means 107 for storing elemental and relational information may be provided in order to deal with increasingly high numbers and scales of thought networks. The thought storage means 107 may comprise a compression facility and an optimization facility that may selectively synthesize thought networks only as needed. Like memories that are brought into focus, acted upon, and then fade away, digital thoughts may need to be retrieved, synthesized into networks to create tangible artifacts such as media, and then disbanded. The thought storage means 107 in the present description teaches a data model used in conjunction with a system of analysis that may be used to integrate thoughts from different domains.

An adaptive classification scheme generator 109 may be used to represent knowledge within specific domains, providing a foundation for reasoning about the concepts. A challenge in generating classification schemes and ontologies is that they must be crafted for specific domains and the cognitive agents that are acting within them. Thoughts may be highly subjective, intensively personal, and often private. Thus the adaptive classification scheme generator 109 may define the scope and application of the type of networks one may create. In the present disclosure, a component for generating highly personalized and domain-specific classification schemes is taught, which may provide the infrastructure for a range of general purpose applications. This component may also function with the thought data stores to integrate thought networks across people and domains, providing the means to recall thoughts, synthesize thought networks as needed, and collapse the networks once the benefits have been culled from it.

Knowledge Representation

In the context of the present invention, knowledge may be represented in several ways known to those skilled in the art. What may be required is a means by which thoughts may be connected such that the thought processor of the present invention may synthesize a network of relationships between the thoughts. The following describes one novel approach of generating a representation of thoughts previously described in PCT application PCT/CA2007/001546, but the thought processor of the present invention may be operable with any representation of thoughts.

Summary of Knowledge Representation Model

For simplicity, the knowledge representation model described in PCT application PCT/CA2007/001546 will be used to explain the novel approach to generating the representation described by the present invention.

A knowledge representation model may include knowledge representation entities, relationships, and methods of transformation.

Entities

FIG. 2 illustrates knowledge representation entities and relationships in one implementation of the invention. For purposes of illustration only, and without limiting the present invention, FIG. 2 also shows sample entity labels 314 in accordance with the knowledge representation entities and relationships 312. The entities may include a set of content nodes 302, a set of content containers 304, a set of concepts 306 (to simplify the illustration, only one concept is presented in FIG. 2), a set of keywords 308, and a set of morphemes 310.

The informational space to be included may be referred to as a domain. A domain may comprise a broad field of interest into which knowledge is divided, and around which boundaries are set.

A domain may be defined without knowing precisely or exhaustively what it contains. The present invention may be used to examine or work over any number of domains. At any particular time, however, it may operate within one selected domain. This may be referred to as the active domain.

The objects of the domain to be classified may be referred to as content nodes 302. Content nodes may be comprised of any objects that are amenable to classification, for example a file, a document, a portion of a document, an image, or a stored string of characters. Content nodes 302 may reference physical objects or virtual objects.

Content nodes 302 may be contained in a set of content containers 304. The content containers 304 may provide addressable (or locatable) information through which content nodes 302 can be retrieved. Content containers 304 may contain one or more content nodes 302. For example, the content container 304 of a Web page, addressable through a URL, may contain many content nodes 302 in the form of text and images.

The meanings of content nodes 302 (for example the description, purpose, usage, or intent of the content node 302) may be abstracted using concepts 306. Concepts 306 may describe content nodes 302 or some aspect of content nodes 302. Individual content nodes 302 may be assigned many concepts 306; individual concepts 306 may be shared across many content nodes 302. In some cases, concepts 306 may not be associated with any content nodes at all. These may include concepts 306 representing some meaning without ascribing that meaning to any specific object or content node 302.

In the context of the present invention, concepts 306 may represent “thoughts” and content nodes 302 may, for example, be text, audio, images, or video associated with a thought.

Concepts 306 may be defined in terms of compound levels of abstraction through their relationships to other entities and structurally in terms of other, more fundamental knowledge representation entities such as keywords 308 and morphemes 310. Such a structure may be referred to as a concept definition. Collectively, the more fundamental knowledge representation entities such as keywords and morphemes that comprise concepts are referred to as attributes of the concept.

Keywords 308 may be more atomic units within a concept 306. Keywords 308 may combine with other keywords 308 to create more complex (and more abstract) concepts 306. Therefore the same keyword may appear in many different concepts 306. For example, the concept “Team Sports” may consist of the keywords “Team” and “Sports”, and the concept “Water Sports” may consist of the keywords “Water” and “Sports”. “Sports” thus may be a keyword for both concepts. The directionality between keywords 308 may also be preserved to partially describe their relationships.

Just as concepts 306 may be broken down into keywords 308, keywords 308 may be broken down further into their most fundamental semantic units, called morphemes 310. Morphemes 310 may be the lowest level of abstraction and represent the most concrete, unambiguous unit of meaning, or semantic unit. For example, “baseball” may be comprised of the morphemes “base-” and “-ball”. By understanding how these building blocks may be related and in what directions, they may be combined into increasingly more complex structures. In this way, all the possible concepts in the domain may be related.

A special category of content nodes 302, referred to as labels (often called “terms” in the art of classification) may be joined to each knowledge representation entity. Each entity in the faceted data set may have a label. The label may be a simplified name, descriptive word or phrase that may be used to identify an entity. Labels are distinct from the respective entities they describe. Labels may simply help make the various entities discernable to humans. The labels assigned to entities are typically drawn from the language and terms presented in the domain.

The labels may not factor into the operations of the automated aspects of the invention, but may have a role in the interaction between the automated aspects and the cognitive agents.

As with content nodes 302, labels may be abstracted from the respective entities they describe in the knowledge representation model. Thus, in FIG. 2 the following types of labels are identified: a content container label 304 a to describe the content container 304; a content node label 302 a to describe the content node 302; a concept label 306 a to describe the concept 306; a set of keyword labels 308 a to describe the set of keywords 308; and a set of morpheme labels 310 a to describe the set of morphemes 310.

Though certain aspects of the operations and system are described with reference to the above knowledge representation model, those of ordinary skill in the art will appreciate that other models may be used, adapting the operations and system accordingly. For example, concepts may be combined together to create higher-order knowledge representation entities (such as “memes”, collections of concepts that make up ideas). The structure of the representation model may also be contracted. For example, the morpheme abstraction layer may be removed such that concepts are defined only in relation to keywords 310.

Relationships

The entity relationships in the input data may emanate from concepts. For example, attributes associated with concepts may be related in a directed fashion (for example broader to narrower), which may be referred to as an attribute hierarchy, linking the associated concepts.

In a domain's attribute hierarchy, each attribute may have zero or more descendants and may have one ancestor (except the root attribute, which has no ancestor).

Generating a Physical Representation of Knowledge

The system of the present invention may require that a data set, for example a faceted data set, that encodes knowledge as “thoughts” and links the thoughts together in a “thought network”, be provided to the thought processing means. There are many methods known to those skilled in the art for providing data as an input to a semantic network such as a thought network. The following description provides one representative example of providing a data set as described in PCT application PCT/CA2007/001546.

It should be noted that the generation of a physical representation of knowledge may be provided across one or more domains; that is, the system and method disclosed by the present invention is operable over both single domains and multiple domains.

FIG. 3 illustrates one example for the process of knowledge representation, which may in this example be referred to as facet analysis. Facet analysis is summarily described below. For simplicity, the knowledge representation model described above will be used to explain the processes involved in facet analysis, however it should be apparent to those skilled in the art that facet analysis, or a similar representation method, may be applied to any knowledge representation model.

Facet analysis may begin by staging 201 a domain, the process of generating a reorganized input structure that represents that collection of data. Depending on the domain, these input structures may embody a range of complexity.

A domain may be, for example, a website comprising several webpages containing an index and library of information. After staging 201, the contents of the website may be represented in a series of overlapping hierarchies (known as a polyhierarchy) that begins with its highest-level (broadest) topics and narrows to its lowest-level (most specific) articles. It could, for illustrative purposes, resemble the following:

-   -   People     -   Places     -   Things         -   Natural things         -   Artificial things         -   Imaginary things             -   . . . .

Following staging 201, an analysis process may consist of exploding the information in a staged domain into its fundamental building blocks, then assembling those building blocks into a much smaller data set for use in the thought processing aspect of the present invention. The result, when using the knowledge representation model of the representative example, may be referred to as a faceted data set.

A faceted data set may encode the classification scheme for the domain, which may utilize the knowledge representation model described above. It may inform the thought processing aspect of the present invention of the possible ways in which semantic concepts in a thought network could potentially be related. Considering the implementation described above, wherein a website is represented, the knowledge may be represented as concepts, keywords, and morphemes as follows.

Each object in the domain (for example, articles, links or pictures) may be associated with one or more concepts that give the object some meaning. The directionality of concept relationships may be captured in a hierarchy. For instance, a website may include the entries “Sports→Team Sports→Baseball”. Each of these topics may be represented by a concept.

Staging

The purpose of the staging phase may be to configure the import job and then extract, transform, and load (“ETL”) the data for use by the analysis tasks that follow. Methods for ETL are known to those skilled in the art. This phase (1) reads data from the data source; (2) translates the raw data into structured data; and (3) stores that structured data in a data storage means. The tools and techniques used for performing these tasks may be dependent on the data source chosen.

Analysis

Analysis may comprise the steps of source structure analytics 203, attribute extraction 205 and attribute hierarchy assembly 207. A representative example is provided based on PCT application PCT/CA2007/001546, and is summarized below for convenience. It should be noted that there are other methods known to those skilled in the art for providing substantially similar results.

After the source data is imported, the source structure analytics 203 phase may be used to analyze and prepare the extracted source data for the later extraction of attributes. This phase may be used to validate and compress the input data using processes, for example removing redundant data, disambiguating concepts based on their shared labels, and removing circular relationships within the data. Such processes are well known in the art.

This helps limit the scope of the input concept relationships to only those of relatively high confidence. For example, relationships that are further removed from the root nodes may be of lower confidence, or relationships may be removed 215 based on context.

Concepts may also be disambiguated 217, that is, concepts with ambiguous labels may be collapsed into the same concept. This may be useful where, for example the implementation utilizing a website, articles share the same label with their parent categories. Disambiguation may comprise: (1) merging or collapsing concepts which share primary labels within the specified relationship sets; or (2) optionally removing alternative labels if they match the primary label of another concept in the relationship set. For further optimization, circular and redundant relationships may optionally be removed 219, 221 or reoriented to eliminate non-hierarchical concept relationships.

Following this process, the extracted data source may be prepared for attribute extraction 205. Using the input concepts as a basis, this phase may create attributes (for example, as keywords) and prepare them for arrangement into an attribute hierarchy. Concepts may be defined by a list of appropriate attributes (for example, as sets of keywords). This process may create 223 one keyword per concept. That keyword may have one label for each concept label. These keyword labels may correspond to concept labels. Alternatively, different labels for a given concept may describe different ideas or aspects of that concept. In this case, a separate keyword may be created for each concept label. Alternatively, these two approaches may be combined. Keywords then may be disambiguated 225, 227 in a similar fashion as concepts may have been disambiguated.

Concept labels may be deconstructed 229 into individual words, which may be removed. This may be accomplished by: (1) beginning with the labels referenced in a previously established index of concept labels; (2) isolating single-word labels from concept labels which are not already tokenized or separated (i.e. those labels not yet present); (3) tokenizing or separating the concept label; and (4) adding the single-word labels (prioritized in order) to keywords that may have been created from the corresponding concepts by the simple keyword extraction process.

Next, new keywords may be assigned to concepts 231 based on labels shared with nearby concepts, thus creating complex concept definitions. Words previously identified in an index of “common words” may be excluded from being added. Finally, unused labels may optionally be removed 233.

Attribute relationships may be extracted and refined 209 by classifying each concept as either simple or complex and creating relationships 235 between keywords associated with simple concepts, skipping over any complex concepts in between. A complex concept may be a concept comprising at least one of: more than a threshold number of words in one or all of its labels; or more than one keyword. Complex concepts may be excluded because there would be less certainty in their keyword relationships.

Next, an attribute hierarchy may be constructed 211. Source structure analytics may be reapplied to the attribute hierarchy to remove circular 237 or redundant 239 keyword relationships. Attribute (for example, keyword) relationships may be pruned and reorganized 241 into strict hierarchies (where each child keyword may have only one parent). A child keyword having multiple parents may have parent relationships severed, and its ancestor relationships may be reorganized.

Finally, attributes may be assigned 213 to concepts based on concept relationships that present in the input source structure. Prior to exporting, concept definitions may be revised 243 such that each of the attributes (for example, keywords in a parent's concept definition are one of the following: (1) found in the child concept definition; or (2) related to a keyword in the child concept definition. If the concept definition does not meet this criterion, then it may be changed 245 so that it does.

The output 247 of the staging and analysis process may be an attribute hierarchy in a format usable by the thought processor. The format may be a plurality of indices or tables. The plurality of tables may include those to store entities (for example concepts comprised of attributes of keywords and morphemes), relationships between the attributes, content nodes and URI references to their source content containers, labels for each entity, and other information that may be useful, for example process metadata.

Thought Processor

The following describes one novel implementation of the thought network aspect of the thought processor, which may be based upon the methods previously described in PCT application PCT/CA2007/001546, but the thought processor of the present invention may be operable with any thought network which could, for example, also be based upon formal concept analysis or facet classification synthesis.

FIG. 4 illustrates one implementation of the thought network synthesis aspect of the thought processor.

The thought processor aspect of the present invention may accept as input a data set 401 (for example the faceted data set described above) representing one or more domains as the areas of interest and gather user input to connect new and existing thoughts together. New semantic networks may be dynamically synthesized using aggregations of data sets provided by the knowledge representation aspect of the present invention.

The output of these synthesis operations may be referred to as a dimensional concept taxonomy, representing dimensional concept relationships through the examination of the attribute hierarchy. Methods of accomplishing this are known to those skilled in the art. Such methods include formal concept analysis and faceted classification synthesis. One possible implementation is now described.

On input, each entity (for example concepts, attributes, content nodes, concept relationships, and attribute relationships) in the faceted data set 401 may be given a unique identifier (ID). Given an entity ID the corresponding label can be determined. From a concept ID, any of the following may be determined: ID list of attributes corresponding to that concept; count of attributes corresponding to that concept; ID list of content nodes corresponding to that concept; count of content nodes corresponding to that concept.

After the dynamic synthesis operations, a number of new mappings 403 may be made including basic lookup functions, attribute hierarchy 415, and concept matching. From the label of a concept, attribute or content node, a list of all the other entities of the same type with the same label may be determined. For each attribute, the set of concepts containing it may be computed, so that from an attribute's ID, the quantity and a list of IDs of that attribute's ancestors, descendants or siblings may be generated. Additionally, given the ID of an attribute or a content node, the quantity and a list of IDs for concepts associated with that entity may be determined. Similarly, given a count of attributes in an attribute set, the quantity and a list of IDs for concepts having attribute sets of that size may be generated.

A user may be presented a user interface enabling the input of one or more parameters to direct the synthesis 405, for example domain, active concept definition, axis definition, and optional limiting parameters to constrain the scope.

User Inputs

FIG. 5. illustrates one aspect of a user interface provided by the present invention.

The inputs provided by the user and the system may include:

-   -   Domain 501: the broad field to which the synthesis operations         should be applied     -   active concept 503: the starting point     -   axis definition 505: the direction in which to search for         related concepts (as either a direction or another point). The         axis definition may be specified using a list of one or more         attributes of the active concept or of some attributes of other         concepts in the domain.

A user may further provide the following parameters using the interface, or these may be provided as set values unrelated to user interactions:

-   -   degrees of separation: maximum number of direct hierarchical         steps from the active concept to a related concept in the         resultant concept hierarchy to progress before stopping. Degrees         of separation may include the steps within hierarchical chains         of relationships.     -   output limits: maximum number of related concepts, content nodes         or both to find in total before stopping. For example, the user         could instruct the synthesis operations to stop after finding         the ten closest concepts, regardless of how many degrees of         separation exist between them and the active concept.

The user may specify any combination of these through the user interface to the system and the system may return a resultant hierarchy of concepts (output concept hierarchy) as a thought network. The output concept hierarchy may then be the focus of further exploration by the user, or may act as a bridge to yet another series of synthesis operations. The further exploration is further described below under the heading Cognitive Agents. The generation of the output concept hierarchy is now more fully explained.

Candidate Set

Referring back to FIG. 4, the active concept's attribute set 407 may be the starting point for finding the set of concepts from within the specified domain that may be used as candidates for the concept hierarchy that is built. The set of concepts that are related to the active concept, without regard to the nature of the relationship, may be referred to as the candidate set. The candidate set may contain concepts whose attributes are within the specified axis definition and degrees of separation, and which are related explicitly or implicitly, or both, to the active concept.

Each concept in the domain may be defined in terms of its attributes, as part of the input faceted data set. There may be a set of attributes for each concept. Some of the attributes in the domain may be related in the attribute hierarchy. Each attribute may be used as part of the definition of multiple concepts. For each attribute, the application may compute the set of concept definitions which contain it.

The attribute set of the active concept may be examined against the attribute hierarchy in order to find explicitly related ancestor and descendant attribute sets. The attribute sets may be constrained to the specified axis definition. They may also be subject to the specified maximum limits and degree of separation distance.

The candidate set may be determined 409 as follows. Attribute sets which are subsets or which have elements that are explicit ancestors of those in the active attribute set, or both, may be considered as possible ancestor concepts. Within each of these related attribute sets, each attribute may have its own set of matching concepts. The intersection set of these concept sets for a given attribute set may contain that attribute set's matching concepts 411.

A similar process may be conducted using related attribute sets which are supersets or which have elements that are explicit descendants, or both, of those in the active attribute set, representing possible descendant concepts. Here again, the intersection set of the concept sets for a related attribute set may contain that attribute set's matching concepts.

The candidate set may be the union of the intersection sets from all the related attribute sets.

Explicit Relationships

Each attribute may have a set of ancestors, a set of descendants, or both. An explicit relationship may exist between attributes when there is a relationship between them as part of the faceted input data in the attribute hierarchy. This relationship can then be used to establish a relationship between the concepts corresponding to these attributes.

For example, given:

-   -   {Attribute 1} is an attribute of “Concept 1”     -   {Attribute 2} is an attribute of “Concept 2”     -   An explicit relationship between these attributes, where         {Attribute 1} is the ancestor and {Attribute 2} is the         descendant

If one replaces the attribute {Attribute 1} with its child attribute {Attribute 2} (hereafter, “operation c”), “Concept 1” would have the same attribute list as “Concept 2” and thus there may be a relationship between them. “Concept 2” may be a descendant of “Concept 1.”

Similarly, if one instead replaces {Attribute 2} with its parent attribute {Attribute 1} (hereafter, “operation p”), then “Concept 2” would have the same attribute list as “Concept 1” and a relationship may exist between them. “Concept 1” may be the ancestor of “Concept 2.”

Implicit Relationships

An implicit relationship may be based on intersecting sets of attributes, derived by adding or deleting one or more attributes from the active concept definition. Using either operation, one may derive the concept definition of a descendant or ancestor. A concept with attributes that are a subset of the active concept may be an ancestor of the active concept. Similarly, a concept with all the attributes of the active concept plus one or more additional attributes may be a descendant of the active concept.

For example, given:

-   -   “Concept 1” is the active concept, and has a concept definition         made up of two attributes: {A, B}     -   “Concept 2” has a concept definition made up of three         attributes: {A, B, C}

If one adds the child attribute {C} to the “Concept 1” attribute list, then “Concept 1” may have the same attribute list as “Concept 2” and there may be an implicit relationship between the two attribute lists. Therefore “Concept 2” may be a descendant of “Concept 1.”

Alternatively, if “Concept 2” was the active concept, one may delete one of its attributes to give it the same attribute list as “Concept 1.” An implicit relationship may then exist between the two attribute lists, where “Concept 1” may be an ancestor of “Concept 2.”

The set of ancestor attribute sets may then be generated by collecting all the subsets of the active attribute set. These subsets may include those with one fewer attribute than the active attribute set, two fewer attributes, three fewer attributes and so on.

Similarly, the set of descendant attribute sets may be generated by collecting all the supersets of the active attribute set. The supersets may include those with one more attribute than the active attribute set, two more attributes, three more attributes and so on.

A hierarchy of explicit attribute relationships may aid the synthesis operations in determining the attribute relationships that hold in the context of every concept definition in which those attributes present.

Derivations

The candidate set may be determined from attribute sets related to the attribute set of the active concept. Explicitly related elements may be found from the attribute hierarchy in the faceted data set. Implicitly related attribute sets may be implied by set intersections. The additional attributes used to find implicit descendant attributes, while in the domain, may or may not be known to the system.

The active attribute set may be paired with each of the attribute sets associated with the concepts in the candidate set. For each pair, a sequence of set operations may be derived which transforms the active attribute set into its paired set. These operations, referred to as derivations, may define how the candidate concept is related to the active concept.

There may be four derivation operations that can be performed 413 on an attribute set in the process of trying to find related attribute sets. The operation types can be abbreviated as shown in Table 1.

TABLE 1 Derivation operation types To make implicit To make explicit relationships relationships with ancestors d: delete an attribute p: replace an attribute with a parent attribute with descendants a: add an attribute c: replace an attribute with a child attribute

Note that the directionality of all the attribute relationships may need to be consistent within pairs of potential concept relationships. Pairs of attribute sets may have ancestor relationships or descendant relationships between their elements, but may not have both.

The synthesis operations may preserve this directionality by only applying either ancestor operations (p, d) or descendant operations (c, a), not both, to establish a relationship between concepts. This may prevent a concept from having all its attributes replaced with those corresponding to an unrelated concept.

For example, given an active concept with attributes {A, B, C} and a candidate concept with attributes {D, B, G, F}, there may be three axes running through the definition of the active concept corresponding to its three attributes. To determine whether a relationship exists between the concepts, one could first use explicit relationships, for example an explicit relationship from A to D, and another from C to G. (These are both c operations: replacing an attribute with a child attribute.) Finally, using the implicit a operation of adding a descendant attribute (namely F) may result in the active concept's attribute set matching that of the candidate descendant. Therefore one may refer to the candidate as a descendant of the active concept.

To illustrate, when pairing the active and candidate attribute sets, there may be three possible groups of attributes:

-   -   those associated with the candidate set only (“candidate-only”         attributes)     -   those associated with both the candidate set and the active set         (“both” attributes)     -   those associated with the active set only (“active-only”         attributes)

If transforming the active set to the candidate set requires deleting “active-only” attributes, then the candidate set may be an ancestor of the active set. If the active set is the same as the candidate set, then the candidate set may be a sibling of the active set. If transforming the active set to the candidate set requires adding “candidate-only” attributes, then the candidate set may be a descendant of the active set.

It may not be valid to transform an active set to a candidate set by both deleting “active-only” attributes and adding “candidate-only” attributes, regardless of whether the two original sets already have attributes in common. Such a pair may be deemed to be unrelated. An exception to this may be when attributes in the “only” sets are related in the attribute hierarchy. In such a case, one of two operations may be performed:

-   -   replace an active set attribute with its parent attribute (with         candidate sets that are ancestors of the active set); or     -   replace an active set attribute with its child attribute (with         candidate sets that are descendants of the active set)

The resulting attribute may then be a member of the “both” set.

Those concepts more likely to be important to the user may be given have higher priority.

Each concept in the candidate set may have a unique derivation series connecting it to the active concept. The order in which derivations are sorted and dealt with by the synthesis operations may affect the ordering of concepts in the result hierarchy. The priority of a candidate concept in the hierarchy may be determined according to Table 2.

TABLE 2 Priority of derivations in determining result hierarchy Prevalence in candidate set Prevalence in domain Explicit operations (p, c) 1 2 Implicit operations (a, d) 3 4

The output of this process 417 may be a thought network operable to process user interactions 419, as more fully described below.

Interface Output to the User

Referring again to FIG. 5, in response to the requirements specified in the user's request, the application may return a concept hierarchy 507, built from concepts that may or may not be associated with objects within the domain, related to the user-provided label's active concept and along the axis. The concept hierarchy may also be used to derive other types of structures and representations, for example bi-directional (associative) relationships between concepts. The user may refer to this concept hierarchy to find concepts related to the active concept they specified. In accordance with the user's interactions with the concept hierarchy, meaningful content can be provided to the user 513.

The derivations may be built into a hierarchical result set. Each node in that hierarchy may represent a concept with an attribute set as its concept definition. Each edge in the hierarchy may represent a single derivation operation.

From within the active domain, the synthesis operations may isolate and return a hierarchy of concepts related to the active concept. The related concepts may branch in both the ancestor (broader) and descendant (more specific) directions from the active concept, along the specified axis and as far as dictated.

Dimensional Structure

In general, concepts and all of their potential relationships may be visualized in terms of a multi-dimensional lattice 509, where the vertices correspond to concepts and the edges correspond to the semantic relations between concepts.

Each of the concepts in the domain may relate to some number of other concepts in that domain, provided the relationship satisfies the synthesis rules as described above. The aggregate of these concepts and their potential relationships, as well as all the multi-dimensional ways to organize these concepts, may be referred to as a dimensional structure.

Axis (Perspective)

In performing synthesis operations, one particular hierarchy of concepts, the direction and extent of which is defined by the axis definition, may be isolated from within the dimensional structure. This may limit the dimensionality of the original lattice to material of interest, thus making it more comprehensible.

A dimensional axis may represent a distinct hierarchy of concept relationships. This hierarchy may represent a slice through two points in the domain space. Like concepts, axes may be defined in terms of attributes. As such, an axis may be specified using two concepts, one of which may be the active concept. This may constrain the set of ancestors that may link to the active concept.

For example, if the domain is “Motor Vehicles”, and the active concept is “my car” which has attributes {grey, Jetta™, mine}, the axis definition could be {grey}, leading to a hierarchy of all vehicles that are grey. Or the axis definition could be {Jetta™}, resulting in a hierarchy of all vehicles that are Jettas™. Or the axis definition could be {mine}, and the hierarchy may be of all vehicles that are owned by me.

Concept Hierarchy

A complex concept may be related to many other concepts in the domain. There may be required a determination of exactly how they are related. Under the system of the present invention, the user may select a domain, active concept definition and active axis definition, and then direct the application to find a set of related concepts.

The result may be a concept hierarchy related to the active concept. The maximum number of relationships found may also be limited by user-specified parameters. For example, the user may constrain the concept hierarchy to a certain number of concepts, or a certain number of content nodes categorized by the concepts. Concept relationships may be hierarchical, directed from broader (parents or ancestors) down to more specific concepts (children or descendents).

For example, one hierarchical branch may be {Industries→Manufacturing→Automobiles}. In this example, the “Industries” concept may be referred to as an ancestor of the active concept, “Manufacturing.” Or, if the active concept is “Manufacturing”, a descendant may be “Automobiles.” Concepts can have zero, one or more ancestors or descendants, or both. Note that in a strict hierarchy, all of the attributes of higher, broader concepts may be inherited by concepts further down the hierarchy. For example, the “Manufacturing” concept may inherit all the attributes of the “Industries” concept.

Tree Structure

The concept hierarchy described above is based on an examination of all the concepts' attributes. A tree structure may be a concept hierarchy where only a subset of the attributes of the active concept is used as the actionable portion of the synthesis operations. The attributes outside of the subset may be ignored. For example, in inferring the organization chart for a company, only those attributes flagged as job titles may be used in a tree structure.

Dimensional Concept Taxonomy

It is possible that some of the possible relationships in the dimensional structure of a given domain will not be of interest to users. Users may be only interested in the particular concept hierarchies that result from the dynamic synthesis operations which they specify.

Over time, the concept hierarchies built for a particular domain may be connected together, forming a subset of the domain's dimensional structure. This subset may grow into a dimensional concept taxonomy comprising that part of the dimensional structure that is of interest. These collections of concept hierarchies may comprise a more extended thought (semantic) network.

Furthermore, as users create these dynamic hierarchies of information, results may be saved and combined into one or more larger and comprehensive dimensional concept taxonomies. Thus, each dimensional concept taxonomy may provide a holistic overview of some or all of the perspectives that one or more users wish to bring to bear on a subject. This taxonomy may have long-term value to users as a knowledge-base of their activities, for their individual and personal use or to be shared with other users.

Virtual Concepts

In some cases, the attribute set at a concept hierarchy node may have no matching concepts. A virtual concept may be used as a placeholder to indicate this.

For example, given an attribute set {A, B, C}, if there is:

-   -   an explicit relationship A→D     -   an explicit relationship D→F     -   no concept with a {D, B, C} attribute set         then {F, B, C} may be in the candidate set with one degree of         separation from {A, B, C}. If {D, B, C} attribute set has no         corresponding concept, there may be a virtual concept at this         node in the hierarchy.

Virtual concepts are more fully described in PCT application PCT/CA2007/001546.

Cognitive Agents

The present invention may provide a means whereby cognitive agents, which may be people, machine-based expert systems, etc., may provide the inputs of pattern recognition and selection that may refine and optimize thought networks over time.

Other alternatives for cognitive agents include (but are not limited to): machine learning; feedback data; and web site analytics. For example, RSS feeds may be provided as the source material for new ‘thoughts’. RSS fields, which are known to those skilled in the art, contain text strings that may be mapped to a process to translate the string to a concept, for example the one described below. These in turn could be used to seed new thoughts into the system.

Label-to-Concept Translation

The process by which the system generates a set of corresponding concept definitions to apply against the active domain may be referred to as label-to-concept translation (LCT).

The basic operations of label-to-concept translation (LCT) may be applied to enable both human-based and machine-based systems. The operations may be generalized to the task of taking a string and converting it to a concept definition. The string may originate as a query submitted by a person (as an articulation of their thought) or it may be derived from machine-generated sources or previously encoded representations of knowledge (for example documents, data feeds, etc.).

The user may be requested by the user interface to provide a thought 503. The thought may be entered as a word or words recognizable to humans. Thus, the user may be providing a label to the thought processor.

The label provided by the user may be translated to one or more concepts for use with the thought processor. Unlike a conventional search and information retrieval mode of operations that returns a ranked list of possible document matches, LCT may dynamically create a concept definition as a representation of a user's thoughts. This LCT generated concept definition may then be used in synthesis operations to create a semantic network (encoded as a concept hierarchy) as the result of the query. The user's thought may be situated within that thought network providing the user with a context for their actions within the network.

Where more than one concept corresponds to the label, the thought processor of the present invention may be required to present a process by which the user may select the appropriate, or desired, concept to disambiguate the meaning. Optionally, the thought processor may return the corresponding concept definitions in order, from those it deems to be the most relevant to those that are the least relevant. Methods of information retrieval that may be used to identify and retrieve candidate attributes from the domain based on their relevance to the user-submitted query are known to those skilled in the art.

LCT enables a concept definition to be derived from the label. This enables the thought to become integrated with data entities by integrating the thought with the semantic network that is wrapped around it by operation of the present invention.

One example of an information retrieval method is now provided.

To translate a user's query label into a list of concept definitions, the following steps may be taken:

-   1. The user's query may be tokenized or separated into individual     words. For example, the user's query “Love and war” would be     tokenized to “Love”, “and” and “war”. -   2. For each tokenized word that is not a stop word (for example     “and”, “the”, etc.), the index may be used to find all the keywords     in the domain that have the word in their labels. -   3. For each word, its keywords may be ranked and the best may be     chosen. For example, a domain may have 9,000 keywords that use the     word, “Love”. The system may use a reasonable number (for     example 10) to present to the user and to create concept     definitions.     -   A set of criteria for ranking a word's keywords may be utilized.         One example of a set of criteria may include:     -   (a) Keywords that exactly match multiple tokenized words. For         example, if the tokenized words are “American” and “Albums”,         then the keyword “American Albums” may rank higher than the         keyword “American” or the keyword “Albums”.     -   (b) Keywords that exactly match the tokenized word. For example,         the word “Love” may be better-matched by the keyword “Love” than         by the keyword “Love in politics”.     -   (c) Keywords that use more than one tokenized word.     -   (d) Keyword prevalence. Keywords that are used by many concepts         in the domain may be ranked higher than keywords used by fewer         concepts. -   4. Concept definitions may be created, corresponding to the selected     keywords. In any concept definition, one and only one keyword from     each word may be used (except noise words, and words for which there     are no keywords). In any concept definition, each keyword may be     used no more than once (even if it maps to more than one tokenized     word).     -   If the user's text query contains unknown words (that is, any         words for which there exist no keywords), then a single virtual         concept with the user's full text query as its label may be         created. It may have no ancestors or descendants. This virtual         concept may have one keyword, whose keyword label also matches         the query string. This virtual concept may be discarded at the         end of the translation process.

For example, if the user's query is “Love and war”, and the word “war” is not used by any of the domain's keywords, the system may create a virtual concept with the label, “Love and war”. This concept would have one keyword, also with the label, “Love and war”. Alternatively, instead of creating virtual concepts for unknown words, the system may be made to instead ignore them.

-   5. The created concept definitions may be ranked. A set of criteria     for ranking concept definitions may be utilized. One example of a     set of criteria may include:     -   (a) Those with the least number of keywords whose labels do not         exactly match the tokenized words. For example, if a user's         query is “Chess Strategy”, then the tokenized words may be         “Chess” and “Strategy”. A concept definition for example         {Keyword 1: “Chess”, Keyword 2: “Strategy”} may have no keywords         whose labels do not exactly match the words. Another concept         definition for example {Keyword 3: “Chess”, Keyword 4: “Strategy         games”} may have one keyword that does not exactly match a         tokenized word. Therefore this second concept definition may         rank lower than the first.     -   (b) Those with the least number of keywords that do not occur         together in a real, pre-existing concept definition.     -   (c) Those with the greatest number of co-occurrences in real         concept definitions.     -   (d) Those whose keywords are more prevalent in the domain. To         determine prevalence, the sum of the occurrences of each of its         keywords in the domain may be calculated for each concept         definition. The sums for each concept definition may then be         compared.         -   If, in a pair of concept definitions being compared, one             concept definition has more keywords than the other, then             that concept definition's least prevalent keywords may be             ignored, so that the same number are being compared from             both concept definitions.     -   (e) Those that match a real concept definition exactly.     -   (f) Those that may be more simple. That is, concept definitions         with fewer keywords may have higher priority.

The concept may then be provided as the active concept to the thought processor, thus enabling the thought processor to provide the user with a hierarchy of related concepts based on the concept definition derived from the label submitted by the user as the query.

Thought Storage

Storage Scheme

Semantic networks in the field of knowledge representation are typically treated as static, permanent structures. In the context of a thought networking system, only the basic building blocks of semantic networks may be required to be stored, such that specific and timely semantic networks may be generated as needed. Thoughts may be stored in a highly compressed data set (the source data, or faceted data set, that comprises the domain). The data may be compressed partly because it may not include the semantic network (specifically, the edges or relationships that constitute the semantic graph), but may only include the nodes (concepts) that can be used as source material for building a semantic network.

Furthermore, the synthesis processes may be configured to acquire source material from any number of domains.

The thought storage aspect of the present invention may provide the means to store faceted data and load it for the thought processor. Storage of data is known to those skilled in the art. One implementation reflecting the knowledge representation model described in PCT application PCT/CA2007/001546 is now described. The data may be organized into three broad areas:

-   -   1. Entities: the main building blocks of knowledge         representation in the system. Entities may include attributes,         concepts, content nodes, and content containers.     -   2. Entity relationships: representing the relationships between         the various entities used in the system. Entity relationships         may include attribute relationships, concept relationships,         concept-attribute relationships, node-concept relationships, and         node-content container relationships.     -   3. Labels: terms used to describe entities that are separated         from the structural definitions of the entities themselves.         Labels may include attribute labels, concept labels, and node         labels. Labels may be shared across the various entities.         Alternatively, labels may be segmented by entity type.

Note that this simplified schema in no way limits the database schema used in any implementation.

Data Encoding

There may be a requirement for transforming something abstract (thoughts) into concrete so that computers (thought processor) and cognitive agents (people) may manipulate them. One example for accomplishing this is now described. A person skilled in the art would recognize that there are many alternative methods to accomplish this.

Faceted output data may be encoded as XML and rendered by XSLT. The faceted output may be reorganized and represented in many different ways. Alternate outputs for representing hierarchies are available.

XSL transformation code (XSLT) may be used to present the presentation layer. All information elements managed by the system (including distributed content if it is channelled through the system) may be rendered by XSLT.

Client-side processing may connect data feeds to the presentation layer of the system. These types of connectors may be used to output information from the application server to the various media that use the structural information. XML data from the application server may be processed through XSLT for presentation on a web page.

In addition to basic publishing and data presentation, XSLT and similar technologies may provide a range of programmatic opportunities. Complex information structures, for example those created by the system may provide actionable information, much like data models. Software programs and agents may act upon the information on the presentation layer, to provide sophistication interactivity and automation. As such, the scope of invention provided by the core structural advantages of the system may extend far beyond the simple publishing.

Those skilled in the art will also appreciate the variability that is possible for architecting these XML and XSLT locations. For example, the files may be stored locally on the computers of end-users or generated using web services. Client-side scripting languages (or similar technology) may be used to insert the information managed by our system on distributed presentation layers (for example the web pages of third-party publishers or software clients).

As another example, an XML data feed containing the core structural information from the system may be combined with the distributed content that the system organizes. Those skilled in the art will appreciate the opportunities to decouple these two types of data into separate data feeds.

These and other architectural opportunities for storing and distributing these presentation files and data feeds are well known in the art, and will therefore not be discussed further herein.

Classification Scheme Generator

Classification scheme generators are a class of technology used to derive, maintain, and aggregate classification schemes. Within thought networking, a classification scheme may provide a means for knowledge representation.

Various classification scheme generators are known to those skilled in the art. For example, open data projects are generating a proliferation of this kind of data, such that one process is the Internet ecosystem itself. There are many other specific technologies for providing classification data (for example, facet analysis, information extraction, etc.).

The classification scheme generator may provide the raw inputs into the thought networking process, thereby providing suggestions, guidance and inspiration to users in the formulation of new thoughts.

A semantic network, once built, may be used as source material for future iterations of thought networking. In other words, the output of one cycle of thought networking may provide the inputs for another cycle. This may manifest in the maintenance of a semantic network as it evolves over time.

User Interface

Referring to FIG. 5, an interface for a thought processor may be provided to a user, or other cognitive agent, for dynamic synthesis of a thought network. The interface may include tools with which to select an active concept definition, an axis definition, an active domain, and various parameters to direct the scope of the synthesis operations, as discussed above.

A user interface may be operable to enable a cognitive agent to input an active concept. The cognitive agent may first input 503 a text label to the user interface. The text label may be translated to the active concept using the process of LCT described above. Where more than one concept corresponds to the label, the user interface may provide a means for the cognitive agent to select the active concept. Otherwise, the result of the LCT process may return the active concept automatically.

Once the active concept is provided, the resultant concept hierarchy 507 may be displayed to the user as a tree with the central node 509 corresponding to the active concept. The scope of the resultant hierarchy 507 may be constrained. For example, one ancestor level and one descendant level may be shown. Other synthesis parameters (for example resolution) and options (for example displaying more or fewer of the concepts in the result hierarchy, showing or hiding virtual concepts, saving the view, or starting a new domain) may be presented to the cognitive agent as well. The cognitive agent may also be presented with a means by which to configure and edit the active concept 503 and active axis definition 505. The user interface may process the active concept as it would with the previously entered active concept.

Upon the selection of the active concept or a related concept, the user interface may be operable to display signature content (i.e. information related to the selected concept). Signature content may, for example, be the useable and useful content associated with the selected concept. The user interface may also include the features required to implement the classification scheme generator described above. This may include means by which a cognitive agent may enable the reclassification of concepts and content.

In a possible usage of the system of the present invention, where the cognitive agent is a user, the user may navigate an informational domain using the following steps. The user may be presented a means to input an active concept 503, for example through a LCT component as described above. The user may enter or select an active concept. The descendant hierarchy may then be shown branching out from the active concept and a set of possible axis definitions (a “tag cloud”) may be displayed 505. Axis labels may be shown in various sizes and shades to represent the density of concepts underneath each, and to express how far away an alternate axis is from the current concept.

The user may enter or select one of the axis definitions 505. The application may display the ancestor hierarchy 507, using the active axis corresponding to one of the tags in the tag cloud. That tag may be displayed as the root 509 of the hierarchy.

The user may continue by selecting another axis definition (by clicking in the tag cloud or entering a new label), active concept (clicking in the tree or entering a new label), resolution or limit and repeating the process.

The hierarchy built may be larger than can be displayed properly in the space-constrained viewing area of a typical output device, for example computer monitor. There may be many possibilities for providing the user with the means to navigate through the result hierarchy. The user could also balance the scope of the synthesis operations against level of detail they seek, by using the resolution parameters (degrees of separation and limit) and successively adding more detail to the resultant hierarchy.

Implementation

The present invention, in one aspect thereof, may be implemented as a computer program. Server application software and databases may execute on both centralized computers and distributed, decentralized systems. The Internet or any other private or public network (for example a company's intranet) may be used as the network to communicate between the centralized servers and the various computing devices and distributed systems that interact with it.

In one aspect of the computer program, the user interface may be provided to a user via a web server that is operable to deliver web pages. The user interface may provide the user a means by which to provide a thought, and be presented with the various labels and axes corresponding to data entities related to the thought. The user may then go through the processes of selection and refinement described above, using the user interface as a means for doing so.

In another aspect of the computer program, the user interface may be provided by client-side software.

The user interface, or front-end, of the present invention may be associated with a server (which may or may not be the same computer as that hosting the front-end) for processing the thought and data set, synthesizing the network, and handling the processes of selection and refinement.

The use of the web server, or client-side software program, and the processing server may provide a means for distributed computing benefits, for example hosted application service provider (ASP) processing models or software-as-a-service (SaaS) application delivery models. Under these models, a third party may offer thought network engine services to domain owners. The domain owner can thus capitalize on the economies of scale that these types of models provide.

The present invention may also be operable over a wireless infrastructure. Present wireless devices are often provided with web browsing capabilities, whether through WAP or traditional means. The user interface of the present invention may be provided to the wireless devices, with processing occurring on the server side of the communication channel or any server associated with such a network.

Further Implementations

There are several useful implementations of a thought network.

Referring to FIG. 5, in one aspect of the present invention, a website implementing a thought network is disclosed. The website may provide a means by which to monetize advertising space 511 to advertisers and advertising networks.

A thought network website may deliver value to advertisers through three basic propositions: (1) content generation; (2) contextual advertisements; and (3) valuable, mass-market audiences.

Additional implementations are also possible for a thought network.

Content Generation

Content generation may facilitate and monetize activities that traditionally have escaped the commercial realm. The present invention may facilitate the completion of a range of activities that used to be poorly supported or unsupported by commercial applications, in an accelerated manner. In particular, the present invention may ease the aggregation and organization of information from different sources in immediately useful ways.

The present invention may create a full paradigm shift in the way people interact with information and use it to catalyze their thoughts and create knowledge. For example, production of vast amounts of new and highly personalized media may be enabled by the present invention.

There is a currently a lack of “content inventory” online in support of online advertising. Two key problems in the online ad market are the lack of quality landing pages and the high price paid by advertisers as a result to place ads on the top websites. This problem is worsening rapidly as the Internet continues to capture an increasing share of interest among advertisers. As a result, advertisers may be seeking quality new content on niche topics and websites catering to the “long tail” (i.e. longer and more specific search queries).

Valuable Mass-Market Audiences

The present invention may cater to the needs of the massive and frustrated audience of users trying to assemble and organize information from various pre-fabricated sources into something that reflects their personal needs. Its natural audience may include the vast segments of knowledge workers, academic and science researchers, students, information architects and library scientists. Additionally, a majority of mainstream web users conduct tasks involving the browsing of numerous websites and combining information, for example to organize travel plans, shop, or dig into any particular topic of interest. The service provided by the present invention may solve the deep-felt problem for these audiences.

Additional Implementations

A thought network may be commercialized in a number of ways, including but not limited to software-as-a-service (SaaS), licensing approaches, data metering, and subscription-based services.

Using SaaS, applications may be provided and priced on a per-user basis. The most up-to-date version may always be provided since the software resides at a centrally controlled location.

Licensing approaches may include providing access to a specific version of an application on a per-user basis. Software updates may also be provided but may require an upgrade fee or the purchase of a new license.

A data metering approach may be provided wherein data is processed and sold as a utility service.

Additionally, a subscription-based approach may be provided wherein access to the thought network application is provided for a fixed term. During this period, usage may be provided on a limited or unlimited basis.

Overview of Consumer-Driven Media Synthesis

The present invention, in another aspect thereof, provides a system, method and computer program that facilitates the dynamic generation of synthesized media through synthesis operations based on a classification system or semantic network, as described above. The present invention enables users to create content in a manner that reflects the way they consume media, easily and in large quantities. The present invention further facilitates the re-use of static data as building blocks for the instantaneous generation of meaningful content based entirely on user input.

The present invention may function so as to facilitate the creation of consumer-directed semantic networks, or thought networks. As shown in FIG. 6, a consumer-directed semantic network or thought network 601 generated in accordance with, for example, the above processes 605 may be utilized as a structural lattice for the media of the network. The consumer-directed semantic network or thought network 601 may be utilized to synthesize media 603. Schema mapping may be utilized to transform a semantic network or thought network 601 to various document formats. Additionally, semantically-annotated content elements of a semantic network or a thought network 601 may be collated within the structural lattice provided by the consumer-generated semantic network.

The present invention overcomes limitations inherent in the prior art. Consumers that require media, which may be in the form of documents, to be created previously had limited options in light of the prior art. First, a consumer could create media from scratch. This process can be time-consuming. Second, a consumer could utilize prior art media synthesis approaches, which are largely based on existing knowledge representations such as ontologies, documents, or other sources. The scope of the synthesis opportunities are bounded by these existing sources, which also limits the ability for consumers to factor in their own unique and personal expression of knowledge. The present invention overcomes these constraints by enabling individual consumers to direct the creation of the media and its contents, and utilize a machine-media synthesis system to catalyze and accelerate this creation process.

The output of the present invention may be synthesized media that may be of many forms or formats, for example documents, web pages, etc. The synthesized media may be unique to an individual consumer. The media also may draw from a variety of sources and is not necessarily constrained by the boundaries of specific media or media sources, for example particular types of documents or document sources.

Semantic networks and/or thought networks, details of which are provided above, may be applied as a tool to assist in the production of synthesized media. Such networks may be put to many uses. For example, thought networking may address at least three broad classes of activities, including: (1) providing a means to acquire, store, and recall thoughts; (2) providing a means to generate highly personalized, contextualized, and utilitarian thought networks as they are needed to do work; and (3) providing software agents to get those jobs done as they consume the thought networks that have been created. As disclosed herein thought networks may be utilized to navigate websites and to search and retrieve information. Such information may be utilized in the creation of synthesized media.

As previously described, semantic networks and/or thought networks may be applied to facilitate content generation. Correspondingly, these networks may be utilized to produce synthesized media. The present invention may incorporate software agents as components of the semantic and/or thought networking service. Software agents may operate on the semantic networks (thought networks) created by consumers. The software agents may be created to transform the semantic networks into various types of media, for example websites, documents, RSS feeds, etc. As shown in FIG. 7, a user interface 701 (that may include the inputs and outputs previously illustrated in FIG. 5) may provide a consumer a means to choose a particular synthesized media output format. For example, a consumer may select the synthesized media output to be a document 703, an RSS feed 705, a website 707, or a mix of different media types and media channels. A skilled reader will recognize that other synthesized media output may be offered to a consumer as output choices.

The system may incorporate several elements and steps. A means may be provided whereby consumer-directed networking may create consumer-directed semantic networks. Documents, or other media, may be synthesized in accordance with consumer-directed semantic networks. As semantic networks are formal, machine-readable data structures, such networks can be transformed into a variety of media and document formats using schema maps and transformation technologies, for example XSLT. Data transformations of this type are well known in the art.

Additionally, content elements of the semantic network may be collated within the structure of the semantic network and may be utilized to fill out the contents of the synthesized media. In this context structured, semantically-annotated content may be associated and/or categorized directly within the semantic network. Information retrieval and/or extraction technologies may be utilized to build associations for unstructured or third-party content. Once the content elements are collated within the structure as required for the media provided by the schema transformation, the generated media may be available for the consumer to review, display, export, edit, or utilize in any other manner.

In one aspect of the present invention, media, for example documents, may be generated automatically, without any interaction by a consumer. Automatic generation may be facilitated by utilization of a surrogate measure for the direction that may be provided by the consumer in accordance with other aspects of the present invention. A surrogate measure may include utilizing an existing media source, for example an existing corpus of documents. Through an analysis of the media source any voids in the structure of the media may be identified. For example, if the media utilized is a corpus of documents, there may be documents within the corpus including information regarding a number of countries and voids in the structure may exist for countries that are not represented. Voids may be utilized as a surrogate for the consumer-directed semantic networking. As disclosed herein, since content can also be automatically collated with the semantic network, the document synthesis may proceed in an end-to-end manner whereby it does not involve any consumer interaction or input.

In another aspect of the present invention, synthesized media may be produced in batches through the application of surrogate measures recursively. An aspect of the present invention that facilitates the production of synthesized media in batches may produce a large quantity of media, for example a large number of documents. Each of the media produced may be presented as possibilities or variations of media for consumer selection. A consumer may select media in a variety of ways. For example, media may be published on the World Wide Web (WWW) and may be indexed by, for example, a search engine such as Google™. By utilizing search queries, consumers may select from the batches of specific types of media, for example particular documents, that meet their individual needs or requirements. The selection of media by a consumer ensures that the consumer accesses media that is of the sort that would have been produced by a consumer-directed media synthesis aspect of the present invention.

Consumer interaction for the purpose of the present invention may involve either direct input by a consumer prior to the production of synthesized media, or selection or choosing of media by a consumer from a batch of synthesized media. A user interface (UI) may be applied as a means whereby consumers may input or select content elements for synthesized media. A UI may also provide a means for consumers to associate content elements within a semantic network. For example, an association may be achieved manually, by explicitly choosing content elements from a categorized list. An association may also be achieved automatically by joining semantically-annotated content elements to nodes within the consumer-directed semantic network. A UI may further provide a means for consumers to select from various online or web-integrable media and document formats. The media formats may be of various types. As shown in FIG. 8 the synthesized media output may be in the form of a webpage. Other outputs may also be generated, for example HTML, RSS, Microsoft™ Word™, PDF, etc. Further outputs may relate to media other than documents, for example audio, video, etc. Consumers may provide their own schema maps to provide for new document formats, style guides, layouts, etc. Consumers may further provide their own information sources for content selection.

The present invention may enable users to create content as they consume media. Through embodiments of the present invention synthesized media may be created easily and may incorporate large quantities of media. The present invention may allow reuse of static data as building blocks for the instantaneous generation of meaningful content based on consumer interactions.

In one aspect of the present invention synthesized media represents transformed semantic or thought networks that may be stored and utilized as a content inventory for monetization purposes. The value of semantic or thought networks may be realized through media synthesis. The synthesized media permits consumer-directed communication which may then be shared with others to facilitate interaction. In this manner, synthesized media may integrate semantic networking within the larger value chain of complementary types of media.

Media synthesis of the present invention may also provide a business marketing and distribution vehicle. Through the production of a variety of media forms it is possible to integrate semantic and thought networks with existing mass market networks. For example mass market networks may include the World Wide Web (WWW), search networks such as Google™, or social networks such as Facebook™. Media integration facilitates the provision of media synthesis services through existing mass market networks.

It will be appreciated by those skilled in the art that other variations of the embodiments described herein may also be practiced without departing from the scope of the invention. Other modifications are therefore possible. 

The invention claimed is:
 1. A computer implemented method for generating a semantic network, the method comprising: representing an information domain as a data set, the data set being defined by data entities and one or more relationships between the data entities; receiving a text query from a human user; and synthesizing, or facilitating the synthesizing of, by one or more computer processors, a semantic network in response to the text query, the synthesizing comprising: translating the text query from the human user into an active concept; including the active concept as a node in the semantic network; deriving relationships between the active concept and selected data entities from the information domain; and populating the semantic network at least in part with the selected data entities from the information domain and the derived relationships between the selected data entities and the active concept.
 2. The computer implemented method of claim 1, further comprising enabling one or more of: (a) interactions between the data entities and data received from the human user; and (b) interactions between the human user and the data entities based on the data received from the human user, by one or more synthesis operations.
 3. The computer implemented method of claim 1, further comprising integrating the active concept from the text query with the data entities.
 4. The computer implemented method of claim 1, further comprising providing the human user with means to traverse the semantic network by selecting data entities related to the active concept.
 5. The computer implemented method of claim 1, further comprising synthesizing the semantic network dynamically in response to the text query received from the human user.
 6. The computer implemented method of claim 4, further comprising storing one or more aspects of learning derived from the semantic network to a storage medium.
 7. The computer implemented method of claim 6, further comprising basing the learning on selecting the data entities related to active concept.
 8. The computer implemented method of claim 6, further comprising storing the one or more aspects of learning for use in dynamic generation of one or more other semantic networks.
 9. The computer implemented method of claim 6, further comprising storing concepts of the semantic network to the storage medium.
 10. The computer implemented method of claim 9, further comprising storing relationships between the concepts in the storage medium.
 11. The computer implemented method of claim 1, wherein translating the query comprises label-to-concept translation.
 12. The computer implemented method of claim 11, wherein the label-to-concept translation comprises defining a label representing the text query, and generating from the label a representation of the active concept.
 13. The computer implemented method of claim 12, wherein the defining the label comprises: (a) allowing the human user to define the label; or (b) obtaining the label from another knowledge representation.
 14. The computer implemented method of claim 11, further comprising applying the label-to-concept translation to synthesizing the semantic network by: separating the label into one or more word components thereof; obtaining from the information domain a plurality of keywords associated with the one or more word components; ranking the keywords based on matching of the keywords with the one or more word components; and creating concept definitions based on the ranking of the keywords.
 15. The computer implemented method of claim 1, further comprising allowing the human user to specify one or more parameters to limit the synthesis of the semantic network being generated.
 16. The computer implemented method of claim 15, wherein the one or more parameters are selected from the group consisting of a domain, an axis direction, one or more maximum degrees of separation, one or more maximum degrees of depth, and a maximum number of network nodes.
 17. The computer implemented method of claim 1, further comprising storing the data set to a storage medium, wherein the data set includes means to create a semantic network.
 18. The computer implemented method of claim 17, further comprising first encoding the data entities, the relationships, and labels, prior to storing the data set to the storage medium.
 19. The computer implemented method of claim 1, further comprising allowing one or more cognitive agents to select one or more parts of the data set to populate one or more domains.
 20. The computer implemented method of claim 1, further comprising synthesizing a second semantic network based on the data entities and relationships in the information domain.
 21. A computer system for generating a semantic network, the computer system comprising: (a) one or more computers configured to receive a text query from a human user and to provide, or provide access to, an information domain represented by a data set defined by data entities and one or more relationships between the data entities; and (b) a thought processor operable to synthesize, or facilitate the synthesis of, by one or more computer processors, a semantic network in response to the text query, the synthesis comprising: translating the text query from the human user into an active concept; including the active concept as a node in the semantic network; deriving relationships between the active concept and selected data entities from the information domain; and populating the semantic network at least in part with the selected data entities from the information domain and the derived relationships between the selected data entities and the active concept.
 22. The computer system of claim 21, wherein the thought processor is operable to synthesize the semantic network dynamically upon receiving the text query from the human user to facilitate new mappings.
 23. The computer system of claim 22, wherein the new mappings provide one or more of the following: basic lookup functions; attribute hierarchy; or concept matching.
 24. At least one non-transitory computer-readable medium storing computer instructions that, when executed by one or more computer processors, perform a method for enabling generation of a semantic network accessible through a web interface that enables a human user to initiate one or more computers to generate the semantic network, the method comprising: (a) obtaining, or obtaining access to, an information domain, the information domain being represented by a data set representing the information domain, the data set being defined by data entities and one or more relationships between the data entities; (b) receiving a text query from the human user; and (c) synthesizing a semantic network in response to the text query, the synthesizing comprising: translating the text query from the human user into an active concept; including the active concept as a node in the semantic network; deriving relationships between the active concept and selected data entities from the information domain; and populating the semantic network at least in part with the selected data entities from the information domain and the derived relationships between the selected data entities and the active concept.
 25. The at least one non-transitory computer-readable medium of claim 24, wherein the web interface is provided by a web application.
 26. A computer implemented method for synthesizing media utilizing a semantic network, the method comprising: (a) generating, or facilitating the generation of, by one or more computer processors, a thought network including: an active concept translated from a text query received from a human user, selected data entities from an information domain, and relationships derived between the active concept and the selected data entities, the generating comprising: including the active concept as a node in the thought network, and populating the thought network at least in part with the selected data entities from the information domain and the derived relationships between the active concept and the selected data entities; and (b) transforming the thought network to generate and provide one or more forms of synthesized media to the human user.
 27. The computer implemented method of claim 26, further comprising providing client-directed synthesized media based on a consumer-directed interaction whereby the consumer performs one of the following steps: (a) providing input to direct the generation of the synthesized media; or (b) selecting synthesized media from the one or more forms provided.
 28. The computer implemented method of claim 26, wherein the synthesized media is web-integrable media.
 29. The computer implemented method of claim 26, further comprising storing the synthesized media as a content inventory.
 30. The computer implemented method of claim 26, wherein the transforming comprises auto-generation of synthesized media, wherein the auto-generation does not involve consumer interaction or input.
 31. The computer implemented method of claim 26, wherein the synthesized media is produced in batches.
 32. The computer implemented method of claim 26, further comprising regenerating synthesized media after consumer interaction or input.
 33. The computer implemented method of claim 30, wherein auto-generation is facilitated by utilization of a surrogate measure for direction provided by the consumer.
 34. The computer implemented method of claim 26, further comprising re-using static data as building blocks for an instantaneous generation of synthesized media based entirely on consumer input.
 35. The computer implemented method of claim 26, wherein the transforming is performed by one or more software agents.
 36. The computer implemented method of claim 26, further comprising providing a user interface for the consumer to input or select content elements.
 37. The computer implemented method of claim 26, further comprising making the generated media available for the consumer to display, export and edit.
 38. The computer implemented method of claim 26, further comprising integrating the generated media with mass market networks.
 39. The computer implemented method of claim 1, wherein the data entities comprise media or content.
 40. The computer implemented method of claim 39, wherein the content comprises text, audio, images, and/or video.
 41. The computer implemented method of claim 1, wherein the act of synthesizing a semantic network comprises facilitating acquisition of, storage of, and recollection of the active concept and/or existing synthesized semantic networks related to the active concept.
 42. The computer implemented method of claim 1, wherein synthesizing the semantic network comprises creating both nodes and edges to complement reasoning across existing semantic networks.
 43. The computer implemented method of claim 1, wherein the synthesizing comprises acquiring source material from any number of information domains.
 44. The computer implemented method of claim 1, further comprising dynamically synthesizing new semantic networks using aggregations of data sets.
 45. The computer implemented method of claim 33, wherein utilizing the surrogate measure comprises utilizing an existing media source. 